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The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression

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Major Depressive Disorder

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1305))

Summary

Major depressive disorder (MDD) shows a high prevalence and is associated with increased disability. While traditional studies aimed to investigate global characteristic neurobiological substrates of MDD, machine learning-based approaches focus on individual people rather than a group. Therefore, machine learning has been increasingly conducted and applied to clinical practice. Several previous neuroimaging studies used machine learning for stratifying MDD patients from healthy controls as well as in differentially diagnosing MDD apart from other psychiatric disorders. Also, machine learning has been used to predict treatment response using magnetic resonance imaging (MRI) results. Despite the recent accomplishments of machine learning-based MRI studies, small sample sizes and the heterogeneity of the depression group limit the generalizability of a machine learning-based predictive model. Future neuroimaging studies should integrate various materials such as genetic, peripheral, and clinical phenotypes for more accurate predictability of diagnosis and treatment response.

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References

  1. Zhu Y, Qi S, Zhang B, He D, Teng Y, Hu J et al (2019) Connectome-based biomarkers predict subclinical depression and identify abnormal brain connections with the lateral habenula and thalamus. Front Psych 10:371

    Article  Google Scholar 

  2. Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. N Engl J Med 380(14):1347–1358

    Article  PubMed  Google Scholar 

  3. Walter M, Alizadeh S, Jamalabadi H, Lueken U, Dannlowski U, Walter H et al (2019) Translational machine learning for psychiatric neuroimaging. Prog Neuro-Psychopharmacol Biol Psychiatry 91:113–121

    Article  Google Scholar 

  4. World Health Organization (2017) Depression and other common mental disorders: Global health estimates. World Health Organization, Geneva

    Google Scholar 

  5. Hasin DS, Sarvet AL, Meyers JL, Saha TD, Ruan WJ, Stohl M et al (2018) Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiat 75(4):336–346

    Article  Google Scholar 

  6. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders, 5th edn. American Psychiatric Association, Arlington

    Book  Google Scholar 

  7. Kulak-Bejda A, Waszkiewicz N, Bejda G, Zalewska A, Maciejczyk M (2019) Diagnostic value of salivary markers in neuropsychiatric disorders. Dis Markers 2019:4360612

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Keren H, O’Callaghan G, Vidal-Ribas P, Buzzell GA, Brotman MA, Leibenluft E et al (2018) Reward processing in depression: a conceptual and meta-analytic review across fMRI and EEG studies. Am J Psychiatry 175(11):1111–1120

    Article  PubMed  PubMed Central  Google Scholar 

  9. Zhou M, Hu X, Lu L, Zhang L, Chen L, Gong Q et al (2017) Intrinsic cerebral activity at resting state in adults with major depressive disorder: a meta-analysis. Prog Neuro-Psychopharmacol Biol Psychiatry 75:157–164

    Article  Google Scholar 

  10. Wang W, Zhao Y, Hu X, Huang X, Kuang W, Lui S et al (2017) Conjoint and dissociated structural and functional abnormalities in first-episode drug-naive patients with major depressive disorder: a multimodal meta-analysis. Sci Rep 7(1):10401

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Kambeitz J, Cabral C, Sacchet MD, Gotlib IH, Zahn R, Serpa MH et al (2017) Detecting neuroimaging biomarkers for depression: a meta-analysis of multivariate pattern recognition studies. Biol Psychiatry 82(5):330–338

    Article  PubMed  Google Scholar 

  12. Wise T, Radua J, Nortje G, Cleare AJ, Young AH, Arnone D (2016) Voxel-based meta-analytical evidence of structural disconnectivity in major depression and bipolar disorder. Biol Psychiatry 79(4):293–302

    Article  PubMed  Google Scholar 

  13. Videbech P, Ravnkilde B (2004) Hippocampal volume and depression: a meta-analysis of MRI studies. Am J Psychiatry 161(11):1957–1966

    Article  PubMed  Google Scholar 

  14. Santos MAO, Bezerra LS, Carvalho A, Brainer-Lima AM (2018) Global hippocampal atrophy in major depressive disorder: a meta-analysis of magnetic resonance imaging studies. Trends Psychiatry Psychother 40(4):369–378

    Article  PubMed  Google Scholar 

  15. Zhao YJ, Du MY, Huang XQ, Lui S, Chen ZQ, Liu J et al (2014) Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis. Psychol Med 44(14):2927–2937

    Article  PubMed  Google Scholar 

  16. Chen G, Guo Y, Zhu H, Kuang W, Bi F, Ai H et al (2017) Intrinsic disruption of white matter microarchitecture in first-episode, drug-naive major depressive disorder: a voxel-based meta-analysis of diffusion tensor imaging. Prog Neuro-Psychopharmacol Biol Psychiatry 76:179–187

    Article  Google Scholar 

  17. Kim YK, Na KS, Myint AM, Leonard BE (2016) The role of pro-inflammatory cytokines in neuroinflammation, neurogenesis and the neuroendocrine system in major depression. Prog Neuro-Psychopharmacol Biol Psychiatry 64:277–284

    Article  CAS  Google Scholar 

  18. Gao S, Calhoun VD, Sui J (2018) Machine learning in major depression: from classification to treatment outcome prediction. CNS Neurosci Ther 24(11):1037–1052

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hirshfeld-Becker DR, Gabrieli JDE, Shapero BG, Biederman J, Whitfield-Gabrieli S, Chai XJ (2019) Intrinsic functional brain connectivity predicts onset of major depression disorder in adolescence: a pilot study. Brain Connect 9(5):388–398

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lai CH, Wu YT (2015) The gray matter alterations in major depressive disorder and panic disorder: putative differences in the pathogenesis. J Affect Disord 186:1–6

    Article  PubMed  Google Scholar 

  21. Lai CH, Wu YT (2016) The white matter microintegrity alterations of neocortical and limbic association fibers in major depressive disorder and panic disorder: the comparison. Medicine (Baltimore) 95(9):e2982

    Article  Google Scholar 

  22. Lueken U, Straube B, Yang Y, Hahn T, Beesdo-Baum K, Wittchen HU et al (2015) Separating depressive comorbidity from panic disorder: a combined functional magnetic resonance imaging and machine learning approach. J Affect Disord 184:182–192

    Article  PubMed  Google Scholar 

  23. Canu E, Kostic M, Agosta F, Munjiza A, Ferraro PM, Pesic D et al (2015) Brain structural abnormalities in patients with major depression with or without generalized anxiety disorder comorbidity. J Neurol 262(5):1255–1265

    Article  CAS  PubMed  Google Scholar 

  24. Etkin A, Schatzberg AF (2011) Common abnormalities and disorder-specific compensation during implicit regulation of emotional processing in generalized anxiety and major depressive disorders. Am J Psychiatry 168(9):968–978

    Article  PubMed  Google Scholar 

  25. Hilbert K, Lueken U, Muehlhan M, Beesdo-Baum K (2017) Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: a multimodal machine learning study. Brain Behav 7(3):e00633

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hyler SE (2002) APA online CME practice guideline for the treatment of patients with major depressive disorder. J Psychiatr Pract 8(5):315–319

    Article  PubMed  Google Scholar 

  27. Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y et al (2018) Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet 391(10128):1357–1366

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. National Institute of Mental Health. Questions and Answers about the NIMH Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Study — All Medication Levels Bethesda, MD: National Institute of Mental Health; 2006 [Available from: https://www.nimh.nih.gov/funding/clinical-research/practical/stard/allmedicationlevels.shtml

  29. Sinyor M, Schaffer A, Levitt A (2010) The sequenced treatment alternatives to relieve depression (STAR*D) trial: a review. Can J Psychiatr 55(3):126–135

    Article  Google Scholar 

  30. Furukawa TA, Cipriani A, Atkinson LZ, Leucht S, Ogawa Y, Takeshima N et al (2016) Placebo response rates in antidepressant trials: a systematic review of published and unpublished double-blind randomised controlled studies. Lancet Psychiatry 3(11):1059–1066

    Article  PubMed  Google Scholar 

  31. Sawada N, Uchida H, Suzuki T, Watanabe K, Kikuchi T, Handa T et al (2009) Persistence and compliance to antidepressant treatment in patients with depression: a chart review. BMC Psychiatry 9:38

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Thornicroft G, Chatterji S, Evans-Lacko S, Gruber M, Sampson N, Aguilar-Gaxiola S et al (2017) Undertreatment of people with major depressive disorder in 21 countries. Br J Psychiatry 210(2):119–124

    Article  PubMed  PubMed Central  Google Scholar 

  33. Brunello N, Mendlewicz J, Kasper S, Leonard B, Montgomery S, Nelson J et al (2002) The role of noradrenaline and selective noradrenaline reuptake inhibition in depression. Eur Neuropsychopharmacol 12(5):461–475

    Article  CAS  PubMed  Google Scholar 

  34. Singh I, Morgan C, Curran V, Nutt D, Schlag A, McShane R (2017) Ketamine treatment for depression: opportunities for clinical innovation and ethical foresight. Lancet Psychiatry 4(5):419–426

    Article  PubMed  Google Scholar 

  35. Fortney JC, Unutzer J, Wrenn G, Pyne JM, Smith GR, Schoenbaum M et al (2017) A tipping point for measurement-based care. Psychiatr Serv 68(2):179–188

    Article  PubMed  Google Scholar 

  36. Shatte ABR, Hutchinson DM, Teague SJ (2019) Machine learning in mental health: a scoping review of methods and applications. Psychol Med 49(9):1426–1448

    Google Scholar 

  37. Bzdok D, Meyer-Lindenberg A (2018) Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging 3(3):223–230

    PubMed  Google Scholar 

  38. Rush AJ, Trivedi MH, Stewart JW, Nierenberg AA, Fava M, Kurian BT et al (2011) Combining medications to enhance depression outcomes (CO-MED): acute and long-term outcomes of a single-blind randomized study. Am J Psychiatry 168(7):689–701

    Article  PubMed  Google Scholar 

  39. Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH et al (2016) Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3(3):243–250

    Article  PubMed  Google Scholar 

  40. Peterson K, Dieperink E, Anderson J, Boundy E, Ferguson L, Helfand M (2017) Rapid evidence review of the comparative effectiveness, harms, and cost-effectiveness of pharmacogenomics-guided antidepressant treatment versus usual care for major depressive disorder. Psychopharmacology 234(11):1649–1661

    Article  CAS  PubMed  Google Scholar 

  41. Singh AB (2015) Improved antidepressant remission in major depression via a pharmacokinetic pathway polygene pharmacogenetic report. Clin Psychopharmacol Neurosci 13(2):150–156

    Article  PubMed  PubMed Central  Google Scholar 

  42. Rosenblat JD, Lee Y, McIntyre RS (2018) The effect of pharmacogenomic testing on response and remission rates in the acute treatment of major depressive disorder: a meta-analysis. J Affect Disord 241:484–491

    Article  PubMed  Google Scholar 

  43. Groessl EJ, Tally SR, Hillery N, Maciel A, Garces JA (2018) Cost-effectiveness of a pharmacogenetic test to guide treatment for major depressive disorder. J Manag Care Spec Pharm 24(8):726–734

    PubMed  Google Scholar 

  44. Bromis K, Calem M, Reinders A, Williams SCR, Kempton MJ (2018) Meta-analysis of 89 structural MRI studies in posttraumatic stress disorder and comparison with major depressive disorder. Am J Psychiatry 175(10):989–998

    Article  PubMed  PubMed Central  Google Scholar 

  45. Kapur S, Phillips AG, Insel TR (2012) Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Mol Psychiatry 17(12):1174–1179

    Article  CAS  PubMed  Google Scholar 

  46. Chau DT, Fogelman P, Nordanskog P, Drevets WC, Hamilton JP (2017) Distinct neural-functional effects of treatments with selective serotonin reuptake inhibitors, electroconvulsive therapy, and transcranial magnetic stimulation and their relations to regional brain function in major depression: a meta-analysis. Biol Psychiatry Cogn Neurosci Neuroimaging 2(4):318–326

    PubMed  Google Scholar 

  47. Dichter GS, Gibbs D, Smoski MJ (2015) A systematic review of relations between resting-state functional-MRI and treatment response in major depressive disorder. J Affect Disord 172:8–17

    Article  PubMed  Google Scholar 

  48. Lemm S, Blankertz B, Dickhaus T, Muller KR (2011) Introduction to machine learning for brain imaging. NeuroImage 56(2):387–399

    Article  PubMed  Google Scholar 

  49. Patel MJ, Andreescu C, Price JC, Edelman KL, Reynolds CF 3rd, Aizenstein HJ (2015) Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry 30(10):1056–1067

    Article  PubMed  PubMed Central  Google Scholar 

  50. Schnack HG, Kahn RS (2016) Detecting neuroimaging biomarkers for psychiatric disorders: sample size matters. Front Psych 7:50

    Google Scholar 

  51. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders, 5th edn. American Psychiatric Association, Arlington

    Book  Google Scholar 

  52. Leaver AM, Wade B, Vasavada M, Hellemann G, Joshi SH, Espinoza R et al (2018) Fronto-temporal connectivity predicts ECT outcome in major depression. Front Psych 9:92

    Article  Google Scholar 

  53. van Waarde JA, Scholte HS, van Oudheusden LJ, Verwey B, Denys D, van Wingen GA (2015) A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol Psychiatry 20(5):609–614

    Article  PubMed  Google Scholar 

  54. Korgaonkar MS, Rekshan W, Gordon E, Rush AJ, Williams LM, Blasey C et al (2015) Magnetic resonance imaging measures of brain structure to predict antidepressant treatment outcome in major depressive disorder. EBioMedicine 2(1):37–45

    Article  PubMed  Google Scholar 

  55. Wade BS, Joshi SH, Njau S, Leaver AM, Vasavada M, Woods RP et al (2016) Effect of electroconvulsive therapy on striatal morphometry in major depressive disorder. Neuropsychopharmacology 41(10):2481–2491

    Article  PubMed  PubMed Central  Google Scholar 

  56. Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Burger C et al (2016) Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiat 73(6):557–564

    Article  Google Scholar 

  57. Hu X, Zhang L, Hu X, Lu L, Tang S, Li H et al (2018) Abnormal hippocampal subfields may be potential predictors of worse early response to antidepressant treatment in drug-naive patients with major depressive disorder. J Magn Reson Imaging 49(6):1760–1768

    Google Scholar 

  58. Cao B, Luo Q, Fu Y, Du L, Qiu T, Yang X et al (2018) Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder. Sci Rep 8(1):5434

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. De Crescenzo F, Ciliberto M, Menghini D, Treglia G, Ebmeier KP, Janiri L (2017) Is (18)F-FDG-PET suitable to predict clinical response to the treatment of geriatric depression? A systematic review of PET studies. Aging Ment Health 21(9):889–894

    Article  PubMed  Google Scholar 

  60. Koutsouleris N, Kambeitz-Ilankovic L, Ruhrmann S, Rosen M, Ruef A, Dwyer DB et al (2018) Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: a multimodal, multisite machine learning analysis. JAMA Psychiat 75(11):1156–1172

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Psychiatry, Gachon University College of Medicine, Gil Medical Center, Incheon, Republic of Korea

    Kyoung-Sae Na

  2. Department of Psychiatry, Korea University Ansan Hospital, College of Medicine, Ansan, Republic of Korea

    Yong-Ku Kim

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Correspondence to Yong-Ku Kim .

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  1. Department of Psychiatry, Korea University Ansan Hospital, College of Medicine, Ansan, Korea (Republic of)

    Yong-Ku Kim

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Na, KS., Kim, YK. (2021). The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression. In: Kim, YK. (eds) Major Depressive Disorder. Advances in Experimental Medicine and Biology, vol 1305. Springer, Singapore. https://doi.org/10.1007/978-981-33-6044-0_4

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